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| assign_variables |
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| assign_variables |
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| Expand |
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| title | Unsure of how to make this compatible with new login method... |
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Assign individual variables on loginUsers can specify individual variables to assign to the server-side R session. It is best practice to first create a list of the Opal variables you want to analyse. - The example below creates a new variable
myvar that lists the Opal variables required for analysis: LAB_HDL and GENDER - The
variables argument in the function datashield.login uses myvar, which then will call only this list.
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| myvar <- list('LAB_HDL', 'GENDER')
connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D", variables=myvar)
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| Logging into the collaborating servers
Logged in all servers [================================================================] 100% / 4s
Assigning table data...
Assigned all tables [==================================================================] 100% / 7s
Variables assigned:
study1 -- LAB_HDL, GENDER
study2 -- LAB_HDL, GENDER |
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| title | The format of assigned data frames |
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| Assigned data are kept in a data frame (table) named D by default. Each row of the data frame are the individual records and each column is a separate variable. |
- The example below uses the argument
symbol in the datashield.login function to change the name of the data frame from D to mytable
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| myvar <- list('LAB_HDL', 'GENDER')
connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol='mytable', variables=myvar)
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| Code Block |
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| Logging into the collaborating servers
Logged in all servers [================================================================] 100% / 4s
Assigning table data...
Assigned all tables [==================================================================] 100% / 6s
Variables assigned:
study1 -- LAB_HDL, GENDER
study2 -- LAB_HDL, GENDER |
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Conclusion
The other parts in this DataSHIELD tutorial series are:
...